scholarly journals The GFDL Global Ocean and Sea Ice Model OM4.0: Model Description and Simulation Features

2019 ◽  
Vol 11 (10) ◽  
pp. 3167-3211 ◽  
Author(s):  
Alistair Adcroft ◽  
Whit Anderson ◽  
V. Balaji ◽  
Chris Blanton ◽  
Mitchell Bushuk ◽  
...  
2019 ◽  
Vol 12 (8) ◽  
pp. 3745-3758 ◽  
Author(s):  
François Massonnet ◽  
Antoine Barthélemy ◽  
Koffi Worou ◽  
Thierry Fichefet ◽  
Martin Vancoppenolle ◽  
...  

Abstract. The ice thickness distribution (ITD) is one of the core constituents of modern sea ice models. The ITD accounts for the unresolved spatial variability of sea ice thickness within each model grid cell. While there is a general consensus on the added physical realism brought by the ITD, how to discretize it remains an open question. Here, we use the ocean–sea ice general circulation model, Nucleus for European Modelling of the Ocean (NEMO) version 3.6 and Louvain-la-Neuve sea Ice Model (LIM) version 3 (NEMO3.6-LIM3), forced by atmospheric reanalyses to test how the ITD discretization (number of ice thickness categories, positions of the category boundaries) impacts the simulated mean Arctic and Antarctic sea ice states. We find that winter ice volumes in both hemispheres increase with the number of categories and attribute that increase to a net enhancement of basal ice growth rates. The range of simulated mean winter volumes in the various experiments amounts to ∼30 % and ∼10 % of the reference values (run with five categories) in the Arctic and Antarctic, respectively. This suggests that the way the ITD is discretized has a significant influence on the model mean state, all other things being equal. We also find that the existence of a thick category with lower bounds at ∼4 and ∼2 m for the Arctic and Antarctic, respectively, is a prerequisite for allowing the storage of deformed ice and therefore for fostering thermodynamic growth in thinner categories. Our analysis finally suggests that increasing the resolution of the ITD without changing the lower limit of the upper category results in small but not negligible variations of ice volume and extent. Our study proposes for the first time a bi-polar process-based explanation of the origin of mean sea ice state changes when the ITD discretization is modified. The sensitivity experiments conducted in this study, based on one model, emphasize that the choice of category positions, especially of thickest categories, has a primary influence on the simulated mean sea ice states while the number of categories and resolution have only a secondary influence. It is also found that the current default discretization of the NEMO3.6-LIM3 model is sufficient for large-scale present-day climate applications. In all cases, the role of the ITD discretization on the simulated mean sea ice state has to be appreciated relative to other influences (parameter uncertainty, forcing uncertainty, internal climate variability).


2019 ◽  
Author(s):  
Andrew E. Kiss ◽  
Andrew McC. Hogg ◽  
Nicholas Hannah ◽  
Fabio Boeira Dias ◽  
Gary B. Brassington ◽  
...  

2003 ◽  
Vol 5 (2) ◽  
pp. 91-127 ◽  
Author(s):  
S.J. Marsland ◽  
H. Haak ◽  
J.H. Jungclaus ◽  
M. Latif ◽  
F. Röske

2020 ◽  
Author(s):  
Hiroyuki Tsujino ◽  
L. Shogo Urakawa ◽  
Stephen M. Griffies ◽  
Gokhan Danabasoglu ◽  
Alistair J. Adcroft ◽  
...  

Abstract. We present a new framework for global ocean–sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the JRA55-do atmospheric dataset. We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean–ice Reference Experiments (CORE), via the evaluation of OMIP-1 and OMIP-2 simulations from eleven (11) state-of-the-science global ocean–sea-ice models. In the present evaluation, multi-model means are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performances are assessed considering metrics commonly used by ocean modelers. Many features are very similar between OMIP-1 and OMIP-2 simulations, and yet we also identify key improvements in transitioning from OMIP-1 to OMIP-2. For example, the sea surface temperature of the OMIP-2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming hiatus in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean–sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases in the atmospheric forcing. In particular, further efforts are warranted to reduce remaining biases in OMIP-2 such as those related to the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets.


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